import torch
import multiprocessing

input_shape = (416,416)

Cuda = True if torch.cuda.is_available() else False
smoooth_label = 0.05
# annotation_path = 'D:\python\Aclass\\1\myselfV1\data\\train_test.txt'
Dataset_path = 'D:\python\Aclass\\1\myselfV1\data'
class_name = ['heel','maid']
num_class = len(class_name)
anchors = [31, 36,  45, 52,  64, 36,  74, 65,  102, 175,  138, 110,  175, 234,  207, 156,  293, 244]
# model_path = "D:\python\Aclass\\1\myselfV1\logs\Epoch18-Total_Loss2.3896-Val_Loss4.2382.pth"
model_path = 'D:\python\Aclass\yolov5-master\weights\yolov5s.pt'
num_workers = multiprocessing.cpu_count() if False else 0
save_model_dir = 'D:\python\Aclass\\1\myselfV1\logs'
keep_checkpoint_max = 3
f16 = True if Cuda is True else False
save_bool = True
dataset_truth = r'D:\python\upload\learn_yolov4_and_yolov5\logs\save1612.cache'